An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

In light of the escalating global concerns surrounding climate change, the significance of sustainable development in the realm of logistics cannot be overstated. This study undertakes the imperative task of devising strategies aimed at mitigating carbon emissions, reducing logistics costs, minimizing transportation time, and enhancing customer satisfaction. The research delves into the intricacies of an optimization model tailored for a specific iteration of the Location-Routing Problem (LRP), namely the Multi-Objective Multi-Period Low-Carbon Location-Routing Problem (MMLCLRP). This variant of the LRP takes into meticulous consideration several crucial parameters, such as the overall logistics cost, the arrival times of demand points, and carbon emissions. These factors are pivotal in determining both the optimal location for depots and the programming of routes within a multi-period planning horizon. The proposed model guarantees the long-term sustainability of logistics operations while flexibly adapting location routing decisions for each period in response to evolving market demands. To tackle the inherent complexity of this problem, an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed. This approach integrates a pioneering similarity distance metric to quantify the resemblance between potential solutions. Additionally, a crowding clustering strategy was implemented to enhance the diversity within the NSGA-II. Empirical results illustrate the capability of the proposed optimization model in effectively harmonizing various objectives, encompassing economic, efficiency, and environmental aspects within the logistics domain. Additionally, the enhanced algorithm exhibits notable advantages in addressing the complexities inherent in the optimization model.INDEX TERMS An improved NSGA-II, crowding clustering strategy, low-carbon location-routing problem, multi-objective optimization, multi-period.

Section: Optimization Algorithmmentioning

confidence: 99%

A Multi-Objective Multi-Period Low-Carbon Location-Routing Problem: Improved NSGA-II Approach

Chen,

Zhang,

Long

et al. 2024

“…The IMA algorithm in this paper is compared with INSGA-II [24],SPEA-II [25] and MOEA/D [26] algorithms on 40 cases. For a fair comparison, the limit on the number of algorithm iterations is removed and the running time of each algorithm is set to 5 min instead.…”

Section: E Comparison With Other Algorithmsmentioning

confidence: 99%

Green Hybrid Flow Shop Scheduling Problem Considering Sequence Dependent Setup Times and Transportation Times

Shao-xing

Liu

2023

For the green hybrid flow shop scheduling problem considering sequence dependent setup times (SDST) and transportation times, a mixed integer programming model is established with the objectives of minimizing the maximum completion time (makespan) and total energy consumption simultaneously, and an improved memetic algorithm is proposed with the problem characteristics. First of all, an encoding method combining the jobs sequence code at the first stage with the machine allocation code is designed to ensure that the algorithm can search the entire solution space to the greatest extent; then, a mixed population initialization method is designed to improve the quality of the initial population solution; Thirdly, the crossover and mutation operators, and four neighborhood search strategies are designed to balance the global search and local search capabilities of the algorithm; Finally, the effectiveness of the algorithm is verified by numerical experiments.

“…The use of AI for complex production and/or maintenance scheduling is not a new concept, as there are already many AI algorithms explored in the literature. Within the scope of the present paper's problem, the most promising AI algorithms used in the literature are Particle Swarm Optimization (PSO) [20], Linear Programming (LP) [21], Simulated Annealing (SA) [22], Reinforcement Learning (RL) [23], and the most popular being the Genetic Algorithm (GA) [24]- [28]. Accordingly, in the present paper, a GA was chosen to be implemented due to being a well-documented algorithm in the literature for task/load scheduling, much faster when compared to more linear mathematical approaches, and being able to find solutions in large complex solution spaces.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…An integrated flow shop production and preventive maintenance scheduling system using an adaptive local search Nondominated Sorting Genetic Algorithm II (NSGA-II) is proposed in [24]. The balance between production, maintenance activities and degradation is taken into account to minimize completion time and average idle time of machines, reducing production costs.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…However, they fail to further extend the complexity of the problem formulation to include more concepts that enable more reliable simulations of the real-world and efficient utilization of resources. For instance, the work in [24] is only adapted to flow shop manufacturing layouts and does not tackle costs directly, reducing its effectiveness in minimizing costs. Furthermore, the cited works [25] and [26] have a basic approach to the problem formulation, by not considering energy price volatility, constraints imposed in the production schedule, and job shop layouts.…”

Section: B Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Joint Production and Maintenance Scheduling for Total Cost and Machine Overload Reduction in Manufacturing: A Genetic Algorithm Approach

Mota,

Faria,

Ramos

2023

The European manufactory sector has been greatly impacted in recent times by the COVID-19 pandemic and the Russo-Ukrainian conflict, which have made energy prices soar to all-time highs reducing European companies' competitiveness in the global market. To remain competitive in these crises, manufacturing companies need to start optimizing not only their energy costs but also maintenance-derived costs, by better planning maintenance activities, through joint production and maintenance scheduling, and by improving machine longevity by way of reducing overload in single machines. Accordingly, the premise of the present paper is to propose an intelligent joint production and maintenance scheduling system to minimize total costs, that is, energy and maintenance costs, as well as minimize single-machine overload by balancing tasks between machines, while also allowing for imposed constraints in the production schedule. This is achieved through a Genetic Algorithm to solve the scheduling problem in flexible job shop manufacturing layouts. To reduce total costs, retailer energy price volatility, generated renewable energy resources availability and surplus selling, and maintenance stipulated hours prices are considered and benefited from as much as possible. Overload in single machines is reduced by minimizing the machine occupation rate standard deviation in the production schedule. A baseline scenario with real-production data from a work in the literature is used to validate the proposed scheduling system. Obtained results show that the proposed system is able to reliably reduce energy costs by 11.3% up to 15.4%, and single machine overload by 32.3% up to 52.7%.